Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "105" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 50 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 48 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459865 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.782134 | -0.306817 | 0.173938 | -0.013455 | -0.025857 | -0.237075 | -0.735587 | -1.007288 | 0.0722 | 0.0760 | 0.0119 | nan | nan |
| 2459864 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.515142 | -2.153043 | -1.464945 | -0.097337 | -0.283135 | -0.683758 | -0.116370 | -0.556133 | 0.7137 | 0.7001 | 0.3874 | nan | nan |
| 2459863 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.218896 | -0.250962 | -0.291666 | -0.642000 | -0.571328 | 0.996788 | -0.218037 | -0.722849 | 0.7069 | 0.6884 | 0.3828 | nan | nan |
| 2459862 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.334233 | -0.276524 | -1.156140 | 0.046945 | -0.246901 | -0.852572 | -0.311342 | -0.804101 | 0.6903 | 0.7152 | 0.4007 | nan | nan |
| 2459861 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.238146 | 0.018631 | -0.327914 | -0.380594 | -0.390217 | 0.756944 | 0.050874 | -0.632366 | 0.7183 | 0.6951 | 0.3920 | nan | nan |
| 2459860 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.443283 | -0.086059 | -0.565266 | 0.232452 | -0.533892 | -0.891242 | 0.228087 | -0.908507 | 0.7289 | 0.6988 | 0.3862 | nan | nan |
| 2459859 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.225282 | 0.048861 | -0.408982 | -0.333104 | -0.525904 | 0.693991 | -0.036203 | -0.348239 | 0.7322 | 0.7035 | 0.3867 | nan | nan |
| 2459858 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 16.04% | 0.281436 | 0.425197 | -0.409356 | -0.507388 | -0.477313 | 1.221946 | -0.004903 | -0.531388 | 0.7425 | 0.7110 | 0.3961 | 1.995263 | 1.832073 |
| 2459857 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.286536 | -0.751955 | -2.242443 | -2.222332 | 2.572282 | -0.479120 | -0.266794 | -1.440164 | 0.0289 | 0.0264 | 0.0018 | nan | nan |
| 2459856 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.43% | 20.00% | 1.357226 | 0.031998 | -0.195493 | 0.172446 | 0.187840 | -0.439430 | 0.661776 | -0.517367 | 0.7382 | 0.7250 | 0.3813 | 2.079109 | 1.712050 |
| 2459855 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 4.58% | 29.77% | 0.577575 | -0.228427 | -0.611554 | 0.188975 | -0.206108 | -0.352875 | -0.456812 | -1.004235 | 0.7241 | 0.7403 | 0.4099 | 1.908417 | 1.545772 |
| 2459854 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.93% | 0.00% | -0.012395 | -0.008574 | 0.794338 | 1.030826 | -0.708756 | -0.169709 | 0.136056 | -0.694502 | 0.7257 | 0.7567 | 0.4234 | 1.628921 | 1.331553 |
| 2459853 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 19.46% | -0.003441 | 0.616130 | 1.521867 | 1.740468 | -0.366245 | 0.028741 | 0.352534 | 1.413232 | 0.8452 | 0.8515 | 0.2224 | 3.755377 | 3.903885 |
| 2459850 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 18.02% | 18.02% | 0.671909 | 0.057766 | -0.795920 | 3.410853 | -0.022608 | -0.765776 | 0.136702 | -0.883569 | 0.7540 | 0.7777 | 0.3361 | 1.820935 | 1.521282 |
| 2459849 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.640451 | 0.766104 | -0.175234 | 7.912287 | 0.251913 | -0.097264 | 0.924034 | -0.719620 | 0.7530 | 0.7677 | 0.3412 | 4.543089 | 3.759399 |
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 30.15% | 24.62% | 0.857949 | 0.837383 | 0.122710 | 3.155960 | 0.246347 | 0.849867 | 0.071413 | -1.098705 | 0.7380 | 0.7684 | 0.3598 | 1.552361 | 1.338486 |
| 2459847 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459846 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.677686 | 2.307573 | 1.313227 | 5.131481 | 2.648671 | 1.131405 | 0.137408 | -0.305806 | 0.8452 | 0.7011 | 0.4599 | 2.906655 | 2.413451 |
| 2459845 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459843 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459842 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.519495 | -0.793472 | -3.743104 | -2.868066 | -0.656068 | -2.225316 | -0.276951 | -1.123068 | 0.7807 | 0.7269 | 0.2402 | 1.844265 | 1.775483 |
| 2459841 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.512864 | -0.352885 | 0.949620 | 0.503282 | 1.090433 | 1.394977 | 0.323006 | -1.210077 | 0.0290 | 0.0278 | 0.0035 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | -0.520038 | -0.638006 | 14.820559 | 13.260047 | 2.899468 | 1.430008 | 1.242975 | -0.577221 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.179180 | 0.870117 | 7.359648 | 13.747191 | -0.399368 | 2.273229 | -0.110248 | -1.775132 | 0.7713 | 0.7465 | 0.3805 | 5.434491 | 4.258967 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0353 | 0.0382 | 0.0025 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.659148 | -1.119035 | -0.752450 | -0.198649 | -0.399832 | -0.185926 | 0.096805 | -0.475936 | 0.0366 | 0.0390 | 0.0032 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459832 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.948954 | 5.587118 | 7.670534 | 11.106371 | 1.384351 | 2.571353 | -0.625767 | 0.205783 | 0.8196 | 0.5730 | 0.5694 | 3.850480 | 2.843629 |
| 2459831 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459830 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459829 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459828 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459827 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459826 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.098958 | 6.699610 | 11.297572 | 15.825188 | 11.439025 | 17.939068 | -1.040475 | -1.092120 | 0.8187 | 0.6280 | 0.5037 | 6.370515 | 4.973111 |
| 2459825 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.157734 | 5.451554 | 6.504488 | 10.595100 | 3.489925 | 7.837112 | -0.872152 | -0.968961 | 0.8205 | 0.6381 | 0.5090 | 9.748073 | 4.597437 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.252928 | 0.880794 | 10.149998 | 15.602254 | 0.362732 | 1.571501 | 0.040092 | -1.373050 | 0.7458 | 0.7717 | 0.3525 | -0.000000 | -0.000000 |
| 2459823 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459822 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.712164 | 7.276575 | 8.820378 | 13.162964 | 6.486244 | 10.493096 | -0.716106 | -0.623006 | 0.8245 | 0.6623 | 0.5000 | 4.630051 | 3.759704 |
| 2459821 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 4.433139 | 9.493277 | 8.642029 | 13.342233 | 4.688339 | 9.195415 | -1.751128 | -1.682540 | 0.8234 | 0.6743 | 0.4987 | 3.000745 | 2.676439 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.389969 | 3.842865 | 9.851869 | 14.901496 | 12.279032 | 20.289039 | 1.028534 | -1.639725 | 0.7964 | 0.7248 | 0.3931 | 5.347617 | 4.591416 |
| 2459817 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.152887 | 10.319992 | 8.732907 | 12.074578 | 11.235595 | 17.292003 | -0.642643 | 0.184674 | 0.8309 | 0.7060 | 0.4890 | 3.008974 | 2.629484 |
| 2459816 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.840625 | 5.311814 | 9.185166 | 15.194336 | 10.303648 | 19.541426 | 0.224061 | -2.476351 | 0.8585 | 0.6474 | 0.5596 | 3.974387 | 3.190170 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.899158 | 8.683949 | 7.657850 | 12.508548 | 10.153940 | 19.566413 | -0.657611 | -1.505717 | 0.8274 | 0.7132 | 0.5060 | 3.669672 | 3.285104 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 16.635070 | 20.537625 | 18.057044 | 20.396943 | 60.699231 | 69.726066 | -3.207169 | -6.148732 | 0.8119 | 0.7301 | 0.3996 | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Power | 0.173938 | -0.782134 | -0.306817 | 0.173938 | -0.013455 | -0.025857 | -0.237075 | -0.735587 | -1.007288 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | -0.097337 | -2.153043 | -0.515142 | -0.097337 | -1.464945 | -0.683758 | -0.283135 | -0.556133 | -0.116370 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 0.996788 | -0.218896 | -0.250962 | -0.291666 | -0.642000 | -0.571328 | 0.996788 | -0.218037 | -0.722849 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | 0.334233 | 0.334233 | -0.276524 | -1.156140 | 0.046945 | -0.246901 | -0.852572 | -0.311342 | -0.804101 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 0.756944 | 0.018631 | 0.238146 | -0.380594 | -0.327914 | 0.756944 | -0.390217 | -0.632366 | 0.050874 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | 0.443283 | 0.443283 | -0.086059 | -0.565266 | 0.232452 | -0.533892 | -0.891242 | 0.228087 | -0.908507 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 0.693991 | 0.225282 | 0.048861 | -0.408982 | -0.333104 | -0.525904 | 0.693991 | -0.036203 | -0.348239 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 1.221946 | 0.425197 | 0.281436 | -0.507388 | -0.409356 | 1.221946 | -0.477313 | -0.531388 | -0.004903 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Temporal Variability | 2.572282 | -0.751955 | 0.286536 | -2.222332 | -2.242443 | -0.479120 | 2.572282 | -1.440164 | -0.266794 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | 1.357226 | 1.357226 | 0.031998 | -0.195493 | 0.172446 | 0.187840 | -0.439430 | 0.661776 | -0.517367 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | 0.577575 | -0.228427 | 0.577575 | 0.188975 | -0.611554 | -0.352875 | -0.206108 | -1.004235 | -0.456812 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 1.030826 | -0.008574 | -0.012395 | 1.030826 | 0.794338 | -0.169709 | -0.708756 | -0.694502 | 0.136056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 1.740468 | -0.003441 | 0.616130 | 1.521867 | 1.740468 | -0.366245 | 0.028741 | 0.352534 | 1.413232 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 3.410853 | 0.671909 | 0.057766 | -0.795920 | 3.410853 | -0.022608 | -0.765776 | 0.136702 | -0.883569 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 7.912287 | 1.640451 | 0.766104 | -0.175234 | 7.912287 | 0.251913 | -0.097264 | 0.924034 | -0.719620 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 3.155960 | 0.837383 | 0.857949 | 3.155960 | 0.122710 | 0.849867 | 0.246347 | -1.098705 | 0.071413 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 5.131481 | 1.677686 | 2.307573 | 1.313227 | 5.131481 | 2.648671 | 1.131405 | 0.137408 | -0.305806 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | 0.519495 | 0.519495 | -0.793472 | -3.743104 | -2.868066 | -0.656068 | -2.225316 | -0.276951 | -1.123068 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 1.394977 | 0.512864 | -0.352885 | 0.949620 | 0.503282 | 1.090433 | 1.394977 | 0.323006 | -1.210077 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Power | 14.820559 | -0.638006 | -0.520038 | 13.260047 | 14.820559 | 1.430008 | 2.899468 | -0.577221 | 1.242975 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 13.747191 | 0.870117 | 1.179180 | 13.747191 | 7.359648 | 2.273229 | -0.399368 | -1.775132 | -0.110248 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Temporal Discontinuties | 0.096805 | -1.119035 | -0.659148 | -0.198649 | -0.752450 | -0.185926 | -0.399832 | -0.475936 | 0.096805 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 11.106371 | 1.948954 | 5.587118 | 7.670534 | 11.106371 | 1.384351 | 2.571353 | -0.625767 | 0.205783 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 17.939068 | 6.699610 | 3.098958 | 15.825188 | 11.297572 | 17.939068 | 11.439025 | -1.092120 | -1.040475 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 10.595100 | 5.451554 | 2.157734 | 10.595100 | 6.504488 | 7.837112 | 3.489925 | -0.968961 | -0.872152 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 15.602254 | 1.252928 | 0.880794 | 10.149998 | 15.602254 | 0.362732 | 1.571501 | 0.040092 | -1.373050 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 13.162964 | 3.712164 | 7.276575 | 8.820378 | 13.162964 | 6.486244 | 10.493096 | -0.716106 | -0.623006 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Power | 13.342233 | 9.493277 | 4.433139 | 13.342233 | 8.642029 | 9.195415 | 4.688339 | -1.682540 | -1.751128 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 20.289039 | 2.389969 | 3.842865 | 9.851869 | 14.901496 | 12.279032 | 20.289039 | 1.028534 | -1.639725 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 17.292003 | 6.152887 | 10.319992 | 8.732907 | 12.074578 | 11.235595 | 17.292003 | -0.642643 | 0.184674 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 19.541426 | 5.311814 | 2.840625 | 15.194336 | 9.185166 | 19.541426 | 10.303648 | -2.476351 | 0.224061 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 19.566413 | 8.683949 | 3.899158 | 12.508548 | 7.657850 | 19.566413 | 10.153940 | -1.505717 | -0.657611 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 105 | N09 | digital_ok | nn Temporal Variability | 69.726066 | 20.537625 | 16.635070 | 20.396943 | 18.057044 | 69.726066 | 60.699231 | -6.148732 | -3.207169 |